Upload main.py

main.py

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+from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2Model, Wav2Vec2Processor, Wav2Vec2CTCTokenizer
+import torchaudio
+import torch
+from huggingface_hub import notebook_login
+from datasets import load_dataset
+import re
+from transformers import Wav2Vec2ForCTC
+from torch.utils.data import DataLoader
+
+organization_name = "ASR-Erzya-Final-Project"
+#organization_name = "zmmccormick3"
+dataset_name = "asr_erzya_final_data"
+
+dataset = load_dataset(f"{organization_name}/{dataset_name}")
+
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+#://huggingface.co/datasets/ASR-Erzya-Final-Project/asr_erzya_final_data/tree/main
+
+def preprocess_data(batch):
+    inputs = processor(batch["audio"], return_tensors="pt", sampling_rate=16000)
+    targets = processor(batch["text"], return_tensors="pt", padding=True)
+
+    # Ensure that target tensor is of shape (batch_size, sequence_length)
+    targets["input_ids"] = targets["input_ids"].unsqueeze(0)
+
+    return inputs, targets
+
+# Create DataLoader for training data
+train_data_loader = DataLoader(dataset["train"], batch_size=4, collate_fn=preprocess_data)
+
+# Training loop
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
+
+for epoch in range(5):  # Set the number of epochs you want to train for
+    model.train()
+    for batch in train_data_loader:
+        inputs, targets = batch
+        inputs = {key: value.to(device) for key, value in inputs.items()}
+        targets = {key: value.to(device) for key, value in targets.items()}
+
+        optimizer.zero_grad()
+        outputs = model(**inputs, labels=targets["input_ids"])
+        loss = outputs.loss
+        loss.backward()
+        optimizer.step()
+
+# Save the trained model
+model.save_pretrained("your-organization/your-wav2vec-ctc-model")
+processor.save_pretrained("your-organization/your-wav2vec-ctc-model")
+
+
+#common_voice_train = load_dataset(f"{organization_name}/{dataset_name}", split="train")
+#common_voice_test = load_dataset(f"{organization_name}/{dataset_name}", split="test")
+
+# notebook_login()
+
+common_voice_train = load_dataset("common_voice", "myv", split="train")
+common_voice_test = load_dataset("common_voice", "myv", split="test")
+
+# Remove unnecessary columns
+common_voice_train = common_voice_train.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
+common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
+
+chars_to_remove_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']'
+
+def remove_special_characters(batch):
+    batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower()
+    return batch
+
+common_voice_train = common_voice_train.map(remove_special_characters)
+common_voice_test = common_voice_test.map(remove_special_characters)
+
+def replace_hatted_characters(batch):
+    batch["sentence"] = re.sub('[â]', 'a', batch["sentence"])
+    # Add similar lines for other hat characters if needed
+    return batch
+
+common_voice_train = common_voice_train.map(replace_hatted_characters)
+common_voice_test = common_voice_test.map(replace_hatted_characters)
+
+def extract_all_chars(batch):
+    all_text = " ".join(batch["sentence"])
+    vocab = list(set(all_text))
+    return {"vocab": [vocab], "all_text": [all_text]}
+
+vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)
+vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)
+
+vocab_list = list(set(vocab_train["vocab"][0]) | set(vocab_test["vocab"][0]))
+vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}
+vocab_dict["|"] = vocab_dict[" "]
+del vocab_dict[" "]
+vocab_dict["[UNK]"] = len(vocab_dict)
+vocab_dict["[PAD]"] = len(vocab_dict)
+
+import json
+with open('vocab.json', 'w') as vocab_file:
+    json.dump(vocab_dict, vocab_file)
+
+tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("./", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
+
+repo_name = "wav2vec2-large-xls-r-300m-tr-colab" # TK repository name
+tokenizer.push_to_hub(repo_name)
+
+# tokenizer = Wav2Vec2CTCTokenizer.from_pretrained() # TK
+# feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+
+
+def downsample(batch):
+    resample = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
+    batch["audio"] = resample(batch["audio"])
+    return batch
+
+
+common_voice_train = common_voice_train.map(downsample)
+common_voice_test = common_voice_test.map(downsample)
+
+
+feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)
+# processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
+
+'''
+model = Wav2Vec2Model.from_pretrained("Link/to/HuggingfaceModel")
+array, fs = torchaudio.load("/Local/link/to/your/audio.wav"
+input_input = processor(array.squeeze(), sampling_rate=fs, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**input_input)
+print(f"Hidden state shape: {outputs.last_hidden_state.numpy().shape}")
+'''